
import numpy as np
import pandas as pd
import geopandas as gpd
import plotly.express as px
import datetime as dt
import colorcet as ccet
import matplotlib.pyplot as plt

import plotly.io as pio

# This ensures Plotly output works in multiple places:
# plotly_mimetype: VS Code notebook UI
# notebook: "Jupyter: Export to HTML" command in VS Code
# See https://plotly.com/python/renderers/#multiple-renderers
pio.renderers.default = "plotly_mimetype+notebook"


df = pd.read_csv('../data/Call_Data.csv', parse_dates=['Original Time Queued','Arrived Time'])
df['Hour Of Day Queued'] = [x.hour for x in df['Original Time Queued']]

df.head()


px.histogram(df, x='Hour Of Day Queued', color='Precinct', title='SFD Calls By Time Of Day')


df['Response Time'] = [(df['Arrived Time'][x] - df['Original Time Queued'][x]).seconds for x in range(len(df))]
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2086 entries, 0 to 2085
Data columns (total 13 columns):
 #   Column                       Non-Null Count  Dtype         
---  ------                       --------------  -----         
 0   CAD Event Number             2086 non-null   int64         
 1   Event Clearance Description  2086 non-null   object        
 2   Call Type                    2086 non-null   int64         
 3   Priority                     2086 non-null   int64         
 4   Initial Call Type            2086 non-null   object        
 5   Final Call Type              2086 non-null   object        
 6   Original Time Queued         2086 non-null   datetime64[ns]
 7   Arrived Time                 1777 non-null   datetime64[ns]
 8   Precinct                     2086 non-null   object        
 9   Sector                       2086 non-null   object        
 10  Beat                         2086 non-null   object        
 11  Hour Of Day Queued           2086 non-null   int64         
 12  Response Time                1777 non-null   float64       
dtypes: datetime64[ns](2), float64(1), int64(4), object(6)
memory usage: 212.0+ KB


px.histogram(df, x='Response Time', range_x=[0,1200],nbins=1000, labels={'Response Time':'Response Time [seconds]'})


px.density_heatmap(df, x='Hour Of Day Queued', y='Response Time', range_y=[0, 1200], nbinsy=500, nbinsx=24)


import pyproj
gdf = gpd.read_file('../data/SPD_Beats_WGS84.zip')
gdf = gdf.set_index('beat')


## Count the number of calls.
gdf['Call Count'] = [len(df[df['Beat'] == x]) for x in gdf.index]
gdf['Call Count'][gdf['Call Count'] == 0] = np.nan

## Numner of calls per unit area.
gdf['Calls Per Area'] = gdf['Call Count'] / (gdf['st_area_sh']/1e6)

gdf.head()

<ipython-input-25-935fc72e1155>:3: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy


gdf['Average Response Time'] = [np.nanmean(df[df['Beat'] == x]['Response Time']) for x in gdf.index]
gdf.head()

<ipython-input-26-0ca7dfc0965a>:1: RuntimeWarning:

Mean of empty slice


gdf.explore()


fig = plt.figure(figsize=[8,4])

ax1 = fig.add_subplot(1,4,1)
gdf.plot(column='st_area_sh', ax=ax1)

ax2 = fig.add_subplot(1,4,2)
gdf.plot(column='Call Count', ax=ax2)

ax3 = fig.add_subplot(1,4,3)
gdf.plot(column='Calls Per Area', ax=ax3)

ax4 = fig.add_subplot(1,4,4)
gdf.plot(column='Average Response Time', ax=ax4)

ax1.set_title('Area of Beat')
ax2.set_title('Call Count')
ax3.set_title('Calls Per Area')
ax4.set_title('Average Response Time')

Text(0.5, 1.0, 'Average Response Time')


cols = ccet.rainbow4

fig2 = px.choropleth_mapbox(gdf, width=800, height=800, geojson=gdf.geometry, locations=gdf.index, color='Call Count', opacity=0.5, 
                   color_continuous_scale=cols, mapbox_style='open-street-map', center={'lat':47.62,'lon':-122.35}, zoom=10)
fig2.show()


cols = ccet.rainbow4

fig3 = px.choropleth_mapbox(gdf, width=800, height=800, geojson=gdf.geometry, locations=gdf.index, color='Calls Per Area', opacity=0.5, 
                   color_continuous_scale=cols, mapbox_style='open-street-map', center={'lat':47.62,'lon':-122.35}, zoom=10)
fig3.show()


cols = ccet.rainbow4

fig4 = px.choropleth_mapbox(gdf, width=800, height=800, geojson=gdf.geometry, locations=gdf.index, color='Average Response Time', opacity=0.5, 
                   color_continuous_scale=cols, mapbox_style='open-street-map', center={'lat':47.62,'lon':-122.35}, zoom=10)
fig4.show()

	CAD Event Number	Event Clearance Description	Call Type	Priority	Initial Call Type	Final Call Type	Original Time Queued	Arrived Time	Precinct	Sector	Beat	Hour Of Day Queued
0	2022000018060	ASSISTANCE RENDERED	911	1	SFD - ASSIST ON FIRE OR MEDIC RESPONSE	--ASSIST OTHER AGENCY - CITY AGENCY	2022-01-22 13:35:02	2022-01-22 13:49:15	WEST	QUEEN	Q1	13
1	2022000012185	ASSISTANCE RENDERED	911	1	SFD - ASSIST ON FIRE OR MEDIC RESPONSE	--ASSIST PUBLIC - OTHER (NON-SPECIFIED)	2022-01-15 15:42:31	2022-01-15 15:49:33	WEST	DAVID	D3	15
2	2022000270039	REPORT WRITTEN (NO ARREST)	911	1	SFD - ASSIST ON FIRE OR MEDIC RESPONSE	--SUSPICIOUS CIRCUM. - SUSPICIOUS PERSON	2022-10-08 12:02:20	2022-10-08 12:09:22	WEST	DAVID	D3	12
3	2022000270129	ASSISTANCE RENDERED	911	1	SFD - ASSIST ON FIRE OR MEDIC RESPONSE	--ASSIST OTHER AGENCY - CITY AGENCY	2022-10-08 13:52:28	2022-10-08 13:55:53	NORTH	JOHN	J1	13
4	2022000012668	ASSISTANCE RENDERED	911	1	SFD - ASSIST ON FIRE OR MEDIC RESPONSE	--ASSIST OTHER AGENCY - CITY AGENCY	2022-01-16 05:54:11	2022-01-16 06:01:21	NORTH	JOHN	J3	5

	objectid	first_prec	sector	st_area_sh	st_length_	geometry	Call Count	Calls Per Area
beat
99	1	None	99	2.087731e+08	280794.869698	MULTIPOLYGON (((-122.38226 47.74885, -122.3771...	NaN	NaN
B1	2	N	B	3.888917e+07	30766.975027	POLYGON ((-122.40540 47.67583, -122.40536 47.6...	70.0	1.799987
B2	3	N	B	5.514478e+07	32647.183464	POLYGON ((-122.36621 47.67599, -122.36606 47.6...	43.0	0.779766
B3	4	N	B	5.961233e+07	36973.284197	POLYGON ((-122.33801 47.66869, -122.33639 47.6...	32.0	0.536802
C1	5	E	C	3.255283e+07	37950.240898	POLYGON ((-122.31279 47.61416, -122.31279 47.6...	32.0	0.983018

	objectid	first_prec	sector	st_area_sh	st_length_	geometry	Call Count	Calls Per Area	Average Response Time
beat
99	1	None	99	2.087731e+08	280794.869698	MULTIPOLYGON (((-122.38226 47.74885, -122.3771...	NaN	NaN	NaN
B1	2	N	B	3.888917e+07	30766.975027	POLYGON ((-122.40540 47.67583, -122.40536 47.6...	70.0	1.799987	698.530612
B2	3	N	B	5.514478e+07	32647.183464	POLYGON ((-122.36621 47.67599, -122.36606 47.6...	43.0	0.779766	667.450000
B3	4	N	B	5.961233e+07	36973.284197	POLYGON ((-122.33801 47.66869, -122.33639 47.6...	32.0	0.536802	602.666667
C1	5	E	C	3.255283e+07	37950.240898	POLYGON ((-122.31279 47.61416, -122.31279 47.6...	32.0	0.983018	336.103448

Seattle Fire Department Calls and Response¶

What times are busiest?¶

What were response times like throughout the day?¶

Map Visualizations¶

Add column for call count for each beat.¶

Add column for average response time by beat.¶

Quick Map View¶

Interactive Maps¶